Enhancement of the electrochemical performance of silicon/carbon composite material for lithium ion batteries

The silicon/carbon (Si/C) composite material was prepared, and the electrochemical performance was investigated as a promising anode material for lithium ion batteries. The results show that the binder in the electrode acts as an important role for improving the reversible capacity of the Si/C materials during cycling. The Si/C electrode with CMC/SBR binder possesses a better cycle performance than that with PVDF binder. The Si/C composite material shows an initial reversible capacity of more than 700 mAh∙g⁻¹ and remains a reversible capacity of 597 mAh g⁻¹ after 40 cycles.     

Minimizing the trend effect on detrended cross-correlation analysis with empirical mode decomposition

Detrended cross-correlation analysis (DCCA) is a scaling method commonly used to estimate long-range power law cross-correlation in non-stationary signals. However, the susceptibility of DCCA to trends makes the scaling results difficult to analyze due to spurious crossovers. We artificially generate long-range cross-correlated signals and systematically investigate the effect of linear, exponential and periodic trends. Specifically to the crossovers raised by trends, we apply empirical mode decomposition method which decomposes underlying signals into several intrinsic mode functions (IMF) and a residual trend. After the removal of residual term, strong and monotonic trends such as linear and exponential trends are successfully eliminated. But periodic trend cannot be separated out according to the criterion of IMF, which can be eliminated by Fourier transform. As a special case of DCCA, detrended fluctuation analysis presents similar results.     

A self-assembly template approach for preparing hollow carbon microspheres

Hollow carbon microspheres (HCMs) are prepared in a sealed quartz tube via the reaction between ferrocene and ammonium bromide. The morphology and microstructure of the product are characterized by X-ray diffraction, Raman spectroscopy, scanning electron microscopy, focused ion beam workstation, transmission electron microscopy, and differential scanning calorimetry analysis. The diameter of the HCMs ranges from 1 to 13 μm and the thickness of shells ranges from 70 nm to 450 nm. It is concluded that the self-generated spherical droplets of iron amine bromide serve as the core templates for the formation of HCMs.     

一类Schr?dinger算子的连续谱测度

考虑ｌ²（Ｚ）上的离散Ｓｃｈｒ?ｄｉｎｇｅｒ算子，其势Ｖ_n是一个由ｋ＋１个符号｛０，１，２，…，ｋ｝构成的序列．证明了它具有纯连续谱．     

Multiscale time irreversibility analysis of financial time series based on segmentation

Time irreversibility is a subject of increasing interest in an unbalanced system of various time series. Taking into account dynamic basic concepts, we provide multiscale time irreversibility analysis of financial time series based on segmentation which quantifies the time asymmetry in multiscales and is applied to several different forms of financial time series. Specifically, we adopt four distinct time irreversibility indices—Porta’s, Guzik’s and Ehler’s indices (P%, G% and E) and \(\gamma _{2,1} (k)\), respectively, derived from data segments on various timescales. We investigate the performance of our statistical tests for local financial time series from segmented series system with known time reversal properties and find out that it can help classify the partially representative financial markets finally. Particularly, the smaller the scale factor L is the better the ability to distinguish data. Statistical analysis shows a close relationship between G% and E. On the contrary, the connection between P% and G% or P% and E is not proven. In addition, we define a new metric \(\gamma _{2,1} (k)\) to measure the degree of time irreversibility. By further observing the results of the proposed method for computing the degree of irreversibility of the time series, we confirm that the asymmetry is an inherent property of the financial time series, which can be extended to a wide range of scales. Finally, we apply this method to the recurrence plot and multiscale recurrence quantification analysis, to compare effectiveness of the segmentation method.     

Intelligent detection of rail corrugation using ACMP-based energy entropy and LSSVM

In this paper, an intelligent method to diagnose rail corrugation based on signal decomposition and entropy theory is proposed. The axle box acceleration signals are first decomposed into several components with different frequency bands by ACMP, EEMD and MODWT. By comparison, ACMP is able to successfully extract rail corrugation component from original signal without mode mixing. Energy entropy is then introduced here to quantify the degree of the rate of energy concentration. The analysis results show that the energy will change when rail corrugation occurs and the entropy will become small. It has been also proved that the entropy difference of rail corrugation and normal signal based on ACMP is the most significant. In addition, to intelligently diagnose rail corrugation, we combine energy entropy with energy index and the first mode energy, regarded as the input feature vector of LSSVM, to distinguish rail corrugation from mass data sets. It is obvious that the accuracy of ACMP-based technique is the highest.